However, the existing KD methods mainly focus on convolutional neural networks rather than graph neural networks (GNNs). GNNs was proposed to tackle irregular data structures, such as graphs and 3D point clouds, and have been applied from social networks to biomedicine in recent years [1]}, [2]}, [3]}, [4]}, [5]}, [6]}. Since large-scale datasets and comprehensive architectures are required in specific applications, how to design lightweight GNNs becomes an important and challenging research problem. To the best of our knowledge, Yang et al. [7]} made the first attempt to adopt the idea of knowledge distillation to compress GNNs. They proposed a loss called local structure preserving (LSP) to make the student model imitate the teacher model in pairwise similarity of node features.
After that, several subsequent variants extended the local structure into the global structure or redesigned an indicator to capture more complex graph structure information [8]}, [9]}, [10]}.
